FIX imports in Geometric-SMOTE examples and format code (#881)

Author: joaopfonseca

examples/over-sampling/plot_geometric_smote_generation_mechanism.py

@@ -3,8 +3,8 @@
 Data generation mechanism
 =========================
 
-This example illustrates the Geometric SMOTE data 
-generation mechanism and the usage of its 
+This example illustrates the Geometric SMOTE data
+generation mechanism and the usage of its
 hyperparameters.
 
 """
@@ -16,9 +16,7 @@
 import matplotlib.pyplot as plt
 
 from sklearn.datasets import make_blobs
-from imblearn.over_sampling import SMOTE
-
-from gsmote import GeometricSMOTE
+from imblearn.over_sampling import SMOTE, GeometricSMOTE
 
 print(__doc__)
 
@@ -47,11 +45,11 @@ def generate_imbalanced_data(
 def plot_scatter(X, y, title):
     """Function to plot some data as a scatter plot."""
     plt.figure()
-    plt.scatter(X[y == 1, 0], X[y == 1, 1], label='Positive Class')
-    plt.scatter(X[y == 0, 0], X[y == 0, 1], label='Negative Class')
+    plt.scatter(X[y == 1, 0], X[y == 1, 1], label="Positive Class")
+    plt.scatter(X[y == 0, 0], X[y == 0, 1], label="Negative Class")
     plt.xlim(*XLIM)
     plt.ylim(*YLIM)
-    plt.gca().set_aspect('equal', adjustable='box')
+    plt.gca().set_aspect("equal", adjustable="box")
     plt.legend()
     plt.title(title)
 
@@ -66,9 +64,9 @@ def plot_hyperparameters(oversampler, X, y, param, vals, n_subplots):
     for ax, val in zip(ax_arr, vals):
         oversampler.set_params(**{param: val})
         X_res, y_res = oversampler.fit_resample(X, y)
-        ax.scatter(X_res[y_res == 1, 0], X_res[y_res == 1, 1], label='Positive Class')
-        ax.scatter(X_res[y_res == 0, 0], X_res[y_res == 0, 1], label='Negative Class')
-        ax.set_title(f'{val}')
+        ax.scatter(X_res[y_res == 1, 0], X_res[y_res == 1, 1], label="Positive Class")
+        ax.scatter(X_res[y_res == 0, 0], X_res[y_res == 0, 1], label="Negative Class")
+        ax.set_title(f"{val}")
         ax.set_xlim(*XLIM)
         ax.set_ylim(*YLIM)
 
@@ -79,8 +77,8 @@ def plot_comparison(oversamplers, X, y):
     fig, ax_arr = plt.subplots(1, 2, figsize=(15, 5))
     for ax, (name, ovs) in zip(ax_arr, oversamplers):
         X_res, y_res = ovs.fit_resample(X, y)
-        ax.scatter(X_res[y_res == 1, 0], X_res[y_res == 1, 1], label='Positive Class')
-        ax.scatter(X_res[y_res == 0, 0], X_res[y_res == 0, 1], label='Negative Class')
+        ax.scatter(X_res[y_res == 1, 0], X_res[y_res == 1, 1], label="Positive Class")
+        ax.scatter(X_res[y_res == 0, 0], X_res[y_res == 0, 1], label="Negative Class")
         ax.set_title(name)
         ax.set_xlim(*XLIM)
         ax.set_ylim(*YLIM)
@@ -98,7 +96,7 @@ def plot_comparison(oversamplers, X, y):
 X, y = generate_imbalanced_data(
     200, 2, [(-2.0, 2.25), (1.0, 2.0)], 0.25, [-0.7, 2.3], [-0.5, 3.1]
 )
-plot_scatter(X, y, 'Imbalanced data')
+plot_scatter(X, y, "Imbalanced data")
 
 ###############################################################################
 # Geometric hyperparameters
@@ -133,13 +131,13 @@ def plot_comparison(oversamplers, X, y):
 gsmote = GeometricSMOTE(
     k_neighbors=1,
     deformation_factor=0.0,
-    selection_strategy='minority',
+    selection_strategy="minority",
     random_state=RANDOM_STATE,
 )
 truncation_factors = np.array([0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
 n_subplots = [2, 3]
-plot_hyperparameters(gsmote, X, y, 'truncation_factor', truncation_factors, n_subplots)
-plot_hyperparameters(gsmote, X, y, 'truncation_factor', -truncation_factors, n_subplots)
+plot_hyperparameters(gsmote, X, y, "truncation_factor", truncation_factors, n_subplots)
+plot_hyperparameters(gsmote, X, y, "truncation_factor", -truncation_factors, n_subplots)
 
 ###############################################################################
 # Deformation factor
@@ -151,12 +149,12 @@ def plot_comparison(oversamplers, X, y):
 gsmote = GeometricSMOTE(
     k_neighbors=1,
     truncation_factor=0.0,
-    selection_strategy='minority',
+    selection_strategy="minority",
     random_state=RANDOM_STATE,
 )
 deformation_factors = np.array([0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
 n_subplots = [2, 3]
-plot_hyperparameters(gsmote, X, y, 'deformation_factor', truncation_factors, n_subplots)
+plot_hyperparameters(gsmote, X, y, "deformation_factor", truncation_factors, n_subplots)
 
 ###############################################################################
 # Selection strategy
@@ -177,10 +175,10 @@ def plot_comparison(oversamplers, X, y):
     deformation_factor=0.5,
     random_state=RANDOM_STATE,
 )
-selection_strategies = np.array(['minority', 'majority', 'combined'])
+selection_strategies = np.array(["minority", "majority", "combined"])
 n_subplots = [1, 3]
 plot_hyperparameters(
-    gsmote, X, y, 'selection_strategy', selection_strategies, n_subplots
+    gsmote, X, y, "selection_strategy", selection_strategies, n_subplots
 )
 
 ###############################################################################
@@ -193,7 +191,7 @@ def plot_comparison(oversamplers, X, y):
 
 X_new = np.vstack([X, np.array([2.0, 2.0])])
 y_new = np.hstack([y, np.ones(1, dtype=np.int8)])
-plot_scatter(X_new, y_new, 'Imbalanced data')
+plot_scatter(X_new, y_new, "Imbalanced data")
 
 ###############################################################################
 # When the number of ``k_neighbors`` is increased, SMOTE results to the
@@ -202,11 +200,11 @@ def plot_comparison(oversamplers, X, y):
 # ``majority``.
 
 oversamplers = [
-    ('SMOTE', SMOTE(k_neighbors=2, random_state=RANDOM_STATE)),
+    ("SMOTE", SMOTE(k_neighbors=2, random_state=RANDOM_STATE)),
     (
-        'Geometric SMOTE',
+        "Geometric SMOTE",
         GeometricSMOTE(
-            k_neighbors=2, selection_strategy='combined', random_state=RANDOM_STATE
+            k_neighbors=2, selection_strategy="combined", random_state=RANDOM_STATE
         ),
     ),
 ]

examples/over-sampling/plot_geometric_smote_validation_curves.py

@@ -18,12 +18,12 @@
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.svm import LinearSVC
 from sklearn.model_selection import validation_curve
-from sklearn.metrics import make_scorer, cohen_kappa_score
+from sklearn.metrics import make_scorer
 from sklearn.datasets import make_classification
 from imblearn.pipeline import make_pipeline
 from imblearn.metrics import geometric_mean_score
 
-from gsmote import GeometricSMOTE
+from imblearn.over_sampling import GeometricSMOTE
 
 print(__doc__)
 
@@ -81,12 +81,12 @@ def plot_validation_curve(validation_curve_info, scoring_name, title):
     plt.scatter(param_range[idx_max], test_scores_mean[idx_max])
     plt.title(title)
     plt.ylabel(scoring_name)
-    ax.spines['top'].set_visible(False)
-    ax.spines['right'].set_visible(False)
+    ax.spines["top"].set_visible(False)
+    ax.spines["right"].set_visible(False)
     ax.get_xaxis().tick_bottom()
     ax.get_yaxis().tick_left()
-    ax.spines['left'].set_position(('outward', 10))
-    ax.spines['bottom'].set_position(('outward', 10))
+    ax.spines["left"].set_position(("outward", 10))
+    ax.spines["bottom"].set_position(("outward", 10))
     plt.ylim([0.9, 1.0])
 
 
@@ -107,11 +107,11 @@ def plot_validation_curve(validation_curve_info, scoring_name, title):
     DecisionTreeClassifier(random_state=RANDOM_STATE),
 )
 
-scoring_name = 'Geometric Mean Score'
+scoring_name = "Geometric Mean Score"
 validation_curve_info = generate_validation_curve_info(
     gsmote_gbc, X, y, range(1, 8), "geometricsmote__k_neighbors", SCORER
 )
-plot_validation_curve(validation_curve_info, scoring_name, 'K Neighbors')
+plot_validation_curve(validation_curve_info, scoring_name, "K Neighbors")
 
 validation_curve_info = generate_validation_curve_info(
     gsmote_gbc,
@@ -121,7 +121,7 @@ def plot_validation_curve(validation_curve_info, scoring_name, title):
     "geometricsmote__truncation_factor",
     SCORER,
 )
-plot_validation_curve(validation_curve_info, scoring_name, 'Truncation Factor')
+plot_validation_curve(validation_curve_info, scoring_name, "Truncation Factor")
 
 validation_curve_info = generate_validation_curve_info(
     gsmote_gbc,
@@ -131,17 +131,17 @@ def plot_validation_curve(validation_curve_info, scoring_name, title):
     "geometricsmote__deformation_factor",
     SCORER,
 )
-plot_validation_curve(validation_curve_info, scoring_name, 'Deformation Factor')
+plot_validation_curve(validation_curve_info, scoring_name, "Deformation Factor")
 
 validation_curve_info = generate_validation_curve_info(
     gsmote_gbc,
     X,
     y,
-    ['minority', 'majority', 'combined'],
+    ["minority", "majority", "combined"],
     "geometricsmote__selection_strategy",
     SCORER,
 )
-plot_validation_curve(validation_curve_info, scoring_name, 'Selection Strategy')
+plot_validation_curve(validation_curve_info, scoring_name, "Selection Strategy")
 
 ###############################################################################
 # High Imbalance Ratio or low Samples to Features Ratio
@@ -158,11 +158,11 @@ def plot_validation_curve(validation_curve_info, scoring_name, title):
     LinearSVC(random_state=RANDOM_STATE, max_iter=1e5),
 )
 
-scoring_name = 'Geometric Mean Score'
+scoring_name = "Geometric Mean Score"
 validation_curve_info = generate_validation_curve_info(
     gsmote_gbc, X, y, range(1, 8), "geometricsmote__k_neighbors", SCORER
 )
-plot_validation_curve(validation_curve_info, scoring_name, 'K Neighbors')
+plot_validation_curve(validation_curve_info, scoring_name, "K Neighbors")
 
 validation_curve_info = generate_validation_curve_info(
     gsmote_gbc,
@@ -172,7 +172,7 @@ def plot_validation_curve(validation_curve_info, scoring_name, title):
     "geometricsmote__truncation_factor",
     SCORER,
 )
-plot_validation_curve(validation_curve_info, scoring_name, 'Truncation Factor')
+plot_validation_curve(validation_curve_info, scoring_name, "Truncation Factor")
 
 validation_curve_info = generate_validation_curve_info(
     gsmote_gbc,
@@ -182,14 +182,14 @@ def plot_validation_curve(validation_curve_info, scoring_name, title):
     "geometricsmote__deformation_factor",
     SCORER,
 )
-plot_validation_curve(validation_curve_info, scoring_name, 'Deformation Factor')
+plot_validation_curve(validation_curve_info, scoring_name, "Deformation Factor")
 
 validation_curve_info = generate_validation_curve_info(
     gsmote_gbc,
     X,
     y,
-    ['minority', 'majority', 'combined'],
+    ["minority", "majority", "combined"],
     "geometricsmote__selection_strategy",
     SCORER,
 )
-plot_validation_curve(validation_curve_info, scoring_name, 'Selection Strategy')
+plot_validation_curve(validation_curve_info, scoring_name, "Selection Strategy")

imblearn/over_sampling/_smote/tests/test_geometric_smote.py

@@ -31,7 +31,7 @@
 
 
 @pytest.mark.parametrize(
-    'center,surface_point',
+    "center,surface_point",
     [
         (CENTERS[0], SURFACE_POINTS[0]),
         (CENTERS[1], SURFACE_POINTS[1]),
@@ -48,7 +48,7 @@ def test_make_geometric_sample_hypersphere(center, surface_point):
 
 
 @pytest.mark.parametrize(
-    'surface_point,deformation_factor',
+    "surface_point,deformation_factor",
     [
         (np.array([1.0, 0.0]), 0.0),
         (2.6 * np.array([0.0, 1.0]), 0.25),
@@ -68,7 +68,7 @@ def test_make_geometric_sample_half_hypersphere(surface_point, deformation_facto
 
 
 @pytest.mark.parametrize(
-    'center,surface_point,truncation_factor',
+    "center,surface_point,truncation_factor",
     [
         (center, surface_point, truncation_factor)
         for center, surface_point in zip(CENTERS, SURFACE_POINTS)
@@ -94,11 +94,11 @@ def test_make_geometric_sample_line_segment(center, surface_point, truncation_fa
 def test_gsmote_default_init():
     """Test the intialization with default parameters."""
     gsmote = GeometricSMOTE()
-    assert gsmote.sampling_strategy == 'auto'
+    assert gsmote.sampling_strategy == "auto"
     assert gsmote.random_state is None
     assert gsmote.truncation_factor == 1.0
     assert gsmote.deformation_factor == 0.0
-    assert gsmote.selection_strategy == 'combined'
+    assert gsmote.selection_strategy == "combined"
     assert gsmote.k_neighbors == 5
     assert gsmote.n_jobs == 1
 
@@ -119,12 +119,12 @@ def test_gsmote_invalid_selection_strategy():
     X, y = make_classification(
         random_state=RND_SEED, n_samples=n_samples, weights=weights
     )
-    gsmote = GeometricSMOTE(random_state=RANDOM_STATE, selection_strategy='Minority')
+    gsmote = GeometricSMOTE(random_state=RANDOM_STATE, selection_strategy="Minority")
     with pytest.raises(ValueError):
         gsmote.fit_resample(X, y)
 
 
-@pytest.mark.parametrize('selection_strategy', ['combined', 'minority', 'majority'])
+@pytest.mark.parametrize("selection_strategy", ["combined", "minority", "majority"])
 def test_gsmote_nn(selection_strategy):
     """Test nearest neighbors object."""
     n_samples, weights = 200, [0.6, 0.4]
@@ -135,14 +135,14 @@ def test_gsmote_nn(selection_strategy):
         random_state=RANDOM_STATE, selection_strategy=selection_strategy
     )
     _ = gsmote.fit_resample(X, y)
-    if selection_strategy in ('minority', 'combined'):
+    if selection_strategy in ("minority", "combined"):
         assert gsmote.nns_pos_.n_neighbors == gsmote.k_neighbors + 1
-    if selection_strategy in ('majority', 'combined'):
+    if selection_strategy in ("majority", "combined"):
         assert gsmote.nn_neg_.n_neighbors == 1
 
 
 @pytest.mark.parametrize(
-    'selection_strategy, truncation_factor, deformation_factor',
+    "selection_strategy, truncation_factor, deformation_factor",
     [
         (selection_strategy, truncation_factor, deformation_factor)
         for selection_strategy in SELECTION_STRATEGY
@@ -160,7 +160,7 @@ def test_gsmote_fit_resample_binary(
     radius = np.sqrt(0.5) * step
     k_neighbors = 1
     gsmote = GeometricSMOTE(
-        'auto',
+        "auto",
         RANDOM_STATE,
         truncation_factor,
         deformation_factor,
@@ -174,7 +174,7 @@ def test_gsmote_fit_resample_binary(
 
 
 @pytest.mark.parametrize(
-    'selection_strategy, truncation_factor, deformation_factor',
+    "selection_strategy, truncation_factor, deformation_factor",
     [
         (selection_strategy, truncation_factor, deformation_factor)
         for selection_strategy in SELECTION_STRATEGY
@@ -196,7 +196,7 @@ def test_gsmote_fit_resample_multiclass(
     )
     k_neighbors, majority_label = 1, 0
     gsmote = GeometricSMOTE(
-        'auto',
+        "auto",
         RANDOM_STATE,
         truncation_factor,
         deformation_factor,